Method and system for achieving auto adaptive clustering in a sensor network

ABSTRACT

A system and method for achieving auto-adaptive clustering in a sensor network has been explained. The system performs a hierarchical clustering in sensor networks to maximize the lifetime of the network. The system includes a set of sensor nodes and a sink node. The clusters in sensor networks are formed automatically from a large number of deployed nodes where the cluster characteristics are driven by the measurement requirements defined by the end-user. The system also employs a clustering algorithm to achieve adaptive clustering. The processor further includes a first level clustering module for grouping the set of sensor nodes into data level clusters based on the measurements. The processor further includes a second level clustering module for grouping the set of sensor nodes in the data level clusters into the location level clusters based on location. In another embodiment, that clustering can go on to more than two levels.

PRIORITY CLAIM

This U.S. patent application claims priority under 35 U.S.C. § 119 to:India Application No. 201621016128, filed on May 9, 2016. The entirecontents of the aforementioned application are incorporated herein byreference

TECHNICAL FIELD

The embodiments herein generally relates to the field of wireless sensornetworks, and, more particularly, to a method and system for achievingauto-adaptive clustering in a sensor network in an energy efficientmanner using end-user information-level attributes as a criteria forclustering.

BACKGROUND OF THE INVENTION

A wireless sensor network is a technology that detects a behavior and anenvironment of a target by utilizing sensors disposed in a predeterminedregion, converting the detected information into data, and wirelesslytransmitting the data to a sink node collecting data. Generally, asensor network consists of a set of physical nodes that monitor ageographical region for one or more parameters and send the sensedvalues to a central gateway (also called as a sink) that manages thenetwork. Clustering and data aggregation in a sensor networks have sofar grouped sensor nodes based on physical characteristics such aslocation and mobility so that the sensed data could be aggregated andtransmitted to the sink saving energy in the process. The sensornetworks are getting integrated into the larger fabric of Internet ofThings (IoT) with a large number of target applications with varyingrequirements. Existing clustering methods are not adaptive to thecharacteristics of the sensing objective. In order to solve theproblems, research of the sensor network and a data aggregatingtechnology for efficient deployment planning is being conducted.

Clustering in sensor networks and IoT systems have so far primarilyworked with physical parameters associated with clustering such asposition and mobility. Nodes in a typical sensor network deployment areredundant in number, and it is not essential that all sensor nodes aretasked for every sensing requirement. The requirement of more recentsensor network and IoT applications is to monitor systems with a certainservice level agreement (SLA) for the sensing objective, for example, tomonitor the variations in the values of a parameter across a region inan energy efficient manner.

A lot of prior art techniques have been used in the part in the contextof clustering in sensor networks and IoT. They primarily use physicalcharacteristics to perform clustering. Some work on using applicationspecific characteristics such as tags associated with data have beenused to perform optimized data transmission. Work in prior art have notreported to carry out application adaptive clustering and organizationof sensor networks where precision of measurement levels are used as abasis for adaptation and organization of nodes in the network.

SUMMARY OF THE INVENTION

The following presents a simplified summary of some embodiments of thedisclosure in order to provide a basic understanding of the embodiments.This summary is not an extensive overview of the embodiments. It is notintended to identify key/critical elements of the embodiments or todelineate the scope of the embodiments. Its sole purpose is to presentsome embodiments in a simplified form as a prelude to the more detaileddescription that is presented below.

In view of the foregoing, an embodiment herein provides a system forachieving auto-adaptive clustering in a sensor network. The systemcomprises a set of sensor nodes, a user interface, a sink node, memoryand a processor. The set of sensor nodes configured to measure aplurality of parameters, wherein each of the sensor nodes comprising aplurality of sensors present at a plurality of locations in an area. Theuser interface generates a request by a user to send the measuredplurality of parameters. The sink node receives the plurality ofparameters measured by the set of sensor nodes. The processor furthercomprises a first level clustering module, a second level clusteringmodule, a designating module and a conveying module. The first levelclustering module performs a first level clustering for grouping the setof sensor nodes into data level clusters based on the measured pluralityof parameters. The second level clustering module performs a secondlevel clustering for grouping the set of sensor nodes in the data levelclusters into location level clusters based on the plurality oflocations. The designating module designates a cluster head after thedata and location level clustering to arrive at a clustering decision.The conveying module conveys the clustering decision back to the set ofsensor nodes to adaptively rearrange them in clusters.

In another aspect, a method for achieving auto-adaptive clustering in asensor network is provided. Initially in the method a plurality ofparameters is measured by a set of sensor nodes, wherein each of thesensor nodes comprising a plurality of sensors present at a plurality oflocations in an area. At the next step, a request is sent by a userinterface, to the set of sensor nodes to send the plurality ofparameters measured at a plurality of sensor nodes. Further, theplurality of parameters are sent to a sink node in response to therequest. At the next step, a first level clustering is performed by aprocessor, for grouping the set of sensor nodes into data level clustersbased on the measured plurality of parameters. Also, a second levelclustering is performed by the processor, for grouping the set of sensornodes in the data level clusters into location level clusters based onthe plurality of locations. At the next step a node is designated by theprocessor for each of the clusters formed after the data level andlocation level clustering as a cluster head to arrive at a clusteringdecision. And finally, the clustering decision is conveyed back to theset of sensor nodes to adaptively rearrange the set of sensor nodes inclusters.

In yet another aspect, a non-transitory computer-readable medium havingembodied thereon a computer program for executing for achievingauto-adaptive clustering in a sensor network is provided. Initially inthe method a plurality of parameters is measured by a set of sensornodes, wherein each of the sensor nodes comprising a plurality ofsensors present at a plurality of locations in an area. At the nextstep, a request is sent by a user interface, to the set of sensor nodesto send the plurality of parameters measured at a plurality of sensornodes. Further, the plurality of parameters are sent to a sink node inresponse to the request. At the next step, a first level clustering isperformed by a processor, for grouping the set of sensor nodes into datalevel clusters based on the measured plurality of parameters. Also, asecond level clustering is performed by the processor, for grouping theset of sensor nodes in the data level clusters into location levelclusters based on the plurality of locations. At the next step a node isdesignated by the processor for each of the clusters formed after thedata level and location level clustering as a cluster head to arrive ata clustering decision. And finally, the clustering decision is conveyedback to the set of sensor nodes to adaptively rearrange the set ofsensor nodes in clusters.

It should be appreciated by those skilled in the art that any blockdiagram herein represent conceptual views of illustrative systemsembodying the principles of the present subject matter. Similarly, itwill be appreciated that any flow charts, flow diagrams, statetransition diagrams, pseudo code, and the like represent variousprocesses which may be substantially represented in computer readablemedium and so executed by a computing device or processor, whether ornot such computing device or processor is explicitly shown.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein will be better understood from the followingdetailed description with reference to the drawings, in which:

FIG. 1 illustrates a block diagram for achieving auto-adaptiveclustering in a sensor network according to an embodiment of the presentdisclosure;

FIG. 2 illustrates a sensor network deployment setup according to anembodiment of the present disclosure; and

FIG. 3 is a flowchart illustrating the steps involved for achievingauto-adaptive clustering in a sensor network according to an embodimentof the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

The embodiments herein and the various features and advantageous detailsthereof are explained more fully with reference to the non-limitingembodiments that are illustrated in the accompanying drawings anddetailed in the following description. The examples used herein areintended merely to facilitate an understanding of ways in which theembodiments herein may be practiced and to further enable those of skillin the art to practice the embodiments herein. Accordingly, the examplesshould not be construed as limiting the scope of the embodiments herein.

Glossary—Terms Used in the Embodiments

The expression “sensor network” in the context of the present disclosurerefers to a network of interconnected sensor used in any field of theinvention. For example in farming, IoT or any other technology.

The expression “set of sensor nodes” or “sensor node” in the context ofthe present disclosure refers to particular location where plurality ofsensors are present. The sensor node are chosen specifically dependingon the area where the sensor network is deployed.

The expression “sink node” in the context of the present disclosurerefers to the particular location. The sink node acts as a gateway tothe external environment

Referring now to the drawings, and more particularly to FIG. 1 throughFIG. 3, where similar reference characters denote corresponding featuresconsistently throughout the figures, there are shown preferredembodiments and these embodiments are described in the context of thefollowing exemplary system and/or method.

According to an embodiment of the disclosure, a system 100 for achievingauto-adaptive clustering in a sensor network 102 is shown in FIG. 1. Thesystem 100 automatically adapts the energy efficient hierarchicalclustering in the sensor network 102 to maximize the lifetime of thesensor network 102. The clusters in sensor networks are formedautomatically from a large number of deployed nodes where the clustercharacteristics are driven by the measurement requirements defined bythe end-user. The present disclosure uses end-user information-levelattributes as a criteria for clustering.

According to an embodiment of the disclosure, the system 100 consists ofa set of sensors nodes 104, a user interface 106, a sink node 108, amemory 110 and a processor 112 as shown in FIG. 1. The set of sensornodes 104 and the sink node 108 are in communication with the processor108. The set of sensor nodes 104 are deployed for monitoring an area.The set of sensor nodes 104 can monitor particular regions in a field.The sink node 108 acts as a gateway for these nodes with the externalworld. A plurality of sensors (not shown) may be present in the set ofsensor nodes 104. In an example, the plurality of sensors can beattached to a person working in the field. In another example, it shouldbe appreciated that the plurality of sensors may also be presentindependently on the field, capturing the data on the field. A typicalsensor network 102 deployment normally consists of the set of sensornodes 104 talking to the sink node 108 or gateway that manages thenetwork as shown in FIG. 2.

The processor 112 is in communication with the memory 110. The processor112 configured to execute an algorithm stored in the memory 110. Thealgorithm is executed on the sink node 108 and orchestrates the set ofsensor nodes 104 in a specific fashion to achieve hierarchicalapplication specific clustering. To achieve this, the set of sensornodes 104 monitor an area and send the raw measurements to the sink node108. The set of sensor nodes 104 configured to measure a plurality ofparameters, wherein each of the sensor nodes comprising a plurality ofsensors present at a plurality of locations in an area.

According to an embodiment of the disclosure, the user interface 106 isconfigured to generate a request to send the measured plurality ofparameters measured at the set of sensor nodes 104. The user interface106 is operated by a user. The user interface 106 can include a varietyof software and hardware interfaces, for example, a web interface, agraphical user interface, and the like and can facilitate multiplecommunications within a wide variety of networks N/W and protocol types,including wired networks, for example, LAN, cable, etc., and wirelessnetworks, such as WLAN, cellular, or satellite. In an embodiment, theuser interface 106 can include one or more ports for connecting a numberof devices to one another or to another server.

Based on the request generated by the user interface 106, the sink node108 receives the plurality of parameters measured by the set of sensornodes 104. The sink node 108 has a predefined service level agreement(SLA) in terms of precision of monitoring requirements for the regioncovered by the set of sensor nodes 104. The sink node 108 uses thehierarchical clustering algorithm to find the final clusters for the setof sensor nodes 104. The clustering information is conveyed to the nodesin the sensor network 102 so that they orient themselves accordingly andsend the measurements to the sink node 106.

According to an embodiment of the disclosure, the processor 112 furtherincludes a plurality of modules for performing functions. The processor112 may include a first level clustering module 114, a second levelclustering module 116. The processor 112 is configured to process themeasured parameters to arrive at a clustering decision. The first levelclustering module 114 performs a first level clustering for grouping theset of sensor nodes 104 into data level clusters based on the measuredplurality of parameters. The measurement from each of the sensor nodes104 are taken through a transformation function. The output of thetransformation function is used to form clusters where nodes in acluster have similar measurements. As an example, nodes with outputswithin a certain threshold can be considered as belonging to samecluster.

According to another embodiment of the disclosure, the second levelclustering module 116 performs a second level clustering for groupingthe set of sensor nodes 104 in the data level clusters into the locationlevel clusters based on the plurality of locations. The location of eachof the nodes is used as a criteria for sub-clustering each clusters intofurther clusters. The cluster formed after this process have twocharacteristics for nodes: measurements at similar levels and locationclose to each other. The process of auto-adaptive clustering is alsoexplained with the help of an example in the later part of thisdisclosure. It should be appreciated that the clustering is not limitedto two levels, in another embodiments, the clustering can go on to morethan two levels and is not restricted to 2 levels.

According to an embodiment of the disclosure, the designating module 118designates a cluster head after the data and location level clusteringto arrive at a clustering decision. The conveying module 120 thenfurther conveys the clustering decision back to the set of sensor nodes104 to adaptively rearrange the set of sensor nodes 104 in clusters.According to another embodiment of the disclosure, the clustering canalso happen at more than two levels, wherein each level depend on theplurality of parameters and the plurality of locations.

According to an embodiment of the invention, the first level and thesecond level clustering module 114 and 116 executes a clusteringalgorithm called as adaptive data centric clustering algorithm (ADCS)which combines unsupervised learning with an n-level hierarchical datafusion and transfer mechanism to achieve energy efficiency in thenetwork. The ADCS adjusts to the nature, size and cluster-member numbersbased on application level measurement characteristics. With ADCS,contextual clustering was introduced where size of clusters in terms ofnumber of covered nodes within each cluster is governed by the precisionof monitoring requirements as defined by the monitoring/sensingobjectives defined by the end user. The clustering is achieved bystatistically measuring the similarity levels between measurements fromnodes as one of its steps to carry out clustering in order to maximizethe network lifetime

ADCS is executed on the sink node/gateway and is generic enough to allowa variety of analytical models to be used to achieve the contextualclustering. Once the clusters are finalized at the gateway, they aresent back to the network for configuration. Dynamically changingconditions such as this render themselves to modeling with unsupervisedlearning models for analysis.

According to another embodiment of the disclosure, three variants ofADCS algorithm can be used ADCS-DB, ADCS-KM, and ADCSAG (DBSCAN, K Meansand Agglomerative) where each variant uses a different algorithm forunsupervised clustering. More such variants can be derived based on theclustering requirements. For example, a requirement to measure thetemperature of a region with a mean m and with a certain standarddeviation s may form a cluster of a certain size S covering a set ofnodes n. If the standard-deviation is relaxed to 2 s, ADCS may expandthe cluster size to include more nodes.

In operation, a flowchart 200 illustrating the steps involved forachieving auto-adaptive clustering in the sensor network 102 is shown inFIG. 3. Initially at step 202, a plurality of parameters are measured bya set of sensor nodes 104. Each of the set of sensor nodes 104comprising a plurality of sensors present at a plurality of locations inan area. The plurality of sensors are configured to measure variousparameters of the person or the plurality of locations in the area. Atnext step 204, a request is sent by the user interface 106 to the set ofsensor nodes 104 to send the plurality of parameters measured at aplurality of sensor nodes. At step 206, the plurality of parameters aresent to the sink node 108 in response to the request;

At the next step 208 a first level clustering is performed by the firstlevel clustering module 114 for grouping the set of sensor nodes 104into data level clusters based on the measured plurality of parameters.Similarly at step 210, a second level clustering is performed by thesecond level clustering module 116 for grouping the set of sensor nodesin the data level clusters into location level clusters based on theplurality of locations. It should be appreciated that clustering can goon to more than two levels and is not restricted to two levels. At step212, the data level clusters are further divided into location levelclusters and one of the nodes in each cluster is designated as a clusterhead using the designating module 118. And finally at step 214, theclustering decision is conveyed back to the set of sensor nodes 104 toadaptively rearrange the set of sensor nodes in clusters.

According to an embodiment of the disclosure, the system 100 can beexplained with the help of the following example. A region R covered bya set of S sensor nodes where each node s (i) ∈ S measures a parameterregularly at a given interval. Let V (i) denote a set of n measurements{v (1), . . . , v(n)} i from sensor node s (i). For any two s (i), s (j)∈ S where the data points of V (i) have the same trend as V (j), thelocation and field characteristics of the two nodes determine whetherthey could be considered as measuring the same or different conditions.For example, if s (i), s (j) are geographically separated by a largedistance, the similarity in the observed values could be a temporalcoincidence. On the other hand, even if the nodes are placed near eachother, a difference in elevation or overhead vegetation (such as acamouflage) would govern whether the observed trends remain the same orchange over time. There could be several such constraints which governthe extent of similarity and hence clustering. Further, the conditionsaround the nodes may change over time so the clustering decision takenat a given point of time would change at regular intervals. For clarity,the ADCS algorithm have been discussed with clustering at two levels, L0and L1, where L0 is at the data level and L1 at the location level. Theprinciple is generic enough to extend to any number of levels.

ADCS assumes an n node WSN deployment with a sink (gateway) as an endpoint. The ADCS algorithm executes on the sink. It starts with the sinkrequesting all nodes to send their data. The data is processed at thesink to arrive at a clustering decision which is then conveyed back tothe network nodes. It was noted that the network nodes could send theirdata to sink by direct transmission or route it through other nodes inthe network. For clarity it was assumed that a direct transmissionstrategy where a clustering decision implies a set of clusters each witha cluster head and some cluster members. Cluster members transmit tocluster head and cluster head sends to the sink. Clustering at thegateway level incurs a computational overhead which is a function ƒ ofthe number of nodes in the network. The order of f is dependent on thecriteria for degree of similarity of measured data carried out for L0clustering.

The first step after input set generation is L0 clustering to identifythe nodes at similar data-levels. This step gives the scope for anyunsupervised clustering method to be used. The clusters identified inthis manner may be geographically scattered. The input set is passedthrough an L1 cluster which clusters those nodes that are within certainphysical separation of each other. Again there are two ways to achievethis: predefined boundaries and unsupervised clustering spaces. In theabsence of predefined boundaries, unsupervised clustering is carried outto achieve this second-level clustering. Cluster heads are chosenrandomly after L1 clustering. It is possible for some nodes to get leftout during the L0 and L1 clustering process.

Effectively, an adaptive clustering framework was created with ADCSwhich is generic enough to allow a variety of models to be used. Oncethe clusters are finalized at the sink (gateway), they are sent back tothe network for configuration.

According to an embodiment of the present disclosure, the system 100 wassimulated with different versions of the learning models and acomparison is finally drawn and presented. Further, due toaforementioned reasons the nature of data is likely to change over time.Multiple rounds of simulation were performed with changing data valuesto establish the results. Dynamically changing conditions such as thisrender themselves to modeling with unsupervised learning models. ADCShas been configured to use various unsupervised learning algorithms tocarry out L0 and L1 clustering.

The written description describes the subject matter herein to enableany person skilled in the art to make and use the embodiments. The scopeof the subject matter embodiments is defined by the claims and mayinclude other modifications that occur to those skilled in the art. Suchother modifications are intended to be within the scope of the claims ifthey have similar elements that do not differ from the literal languageof the claims or if they include equivalent elements with insubstantialdifferences from the literal language of the claims.

The embodiments of present disclosure herein addresses unresolvedproblem of clustering in sensor networks and IoT. The embodiment, thusprovides a system and method for achieving an auto-adaptive clusteringin the sensor network.

It is, however to be understood that the scope of the protection isextended to such a program and in addition to a computer-readable meanshaving a message therein; such computer-readable storage means containprogram-code means for implementation of one or more steps of themethod, when the program runs on a server or mobile device or anysuitable programmable device. The hardware device can be any kind ofdevice which can be programmed including e.g. any kind of computer likea server or a personal computer, or the like, or any combinationthereof. The device may also include means which could be e.g. hardwaremeans like e.g. an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), or a combination of hardware andsoftware means, e.g. an ASIC and an FPGA, or at least one microprocessorand at least one memory with software modules located therein. Thus, themeans can include both hardware means and software means. The methodembodiments described herein could be implemented in hardware andsoftware. The device may also include software means. Alternatively, theembodiments may be implemented on different hardware devices, e.g. usinga plurality of CPUs.

The embodiments herein can comprise hardware and software elements. Theembodiments that are implemented in software include but are not limitedto, firmware, resident software, microcode, etc. The functions performedby various modules described herein may be implemented in other modulesor combinations of other modules. For the purposes of this description,a computer-usable or computer readable medium can be any apparatus thatcan comprise, store, communicate, propagate, or transport the programfor use by or in connection with the instruction execution system,apparatus, or device.

The medium can be an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system (or apparatus or device) or apropagation medium. Examples of a computer-readable medium include asemiconductor or solid state memory, magnetic tape, a removable computerdiskette, a random access memory (RAM), a read-only memory (ROM), arigid magnetic disk and an optical disk. Current examples of opticaldisks include compact disk-read only memory (CD-ROM), compactdisk-read/write (CD-R/W) and DVD.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output (I/O) devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers. Network adapters mayalso be coupled to the system to enable the data processing system tobecome coupled to other data processing systems or remote printers orstorage devices through intervening private or public networks. Modems,cable modem and Ethernet cards are just a few of the currently availabletypes of network adapters.

A representative hardware environment for practicing the embodiments mayinclude a hardware configuration of an information handling/computersystem in accordance with the embodiments herein. The system hereincomprises at least one processor or central processing unit (CPU). TheCPUs are interconnected via system bus to various devices such as arandom access memory (RAM), read-only memory (ROM), and an input/output(I/O) adapter. The I/O adapter can connect to peripheral devices, suchas disk units and tape drives, or other program storage devices that arereadable by the system. The system can read the inventive instructionson the program storage devices and follow these instructions to executethe methodology of the embodiments herein.

The system further includes a user interface adapter that connects akeyboard, mouse, speaker, microphone, and/or other user interfacedevices such as a touch screen device (not shown) to the bus to gatheruser input. Additionally, a communication adapter connects the bus to adata processing network, and a display adapter connects the bus to adisplay device which may be embodied as an output device such as amonitor, printer, or transmitter, for example.

The preceding description has been presented with reference to variousembodiments. Persons having ordinary skill in the art and technology towhich this application pertains will appreciate that alterations andchanges in the described structures and methods of operation can bepracticed without meaningfully departing from the principle, spirit andscope.

What is claimed is:
 1. A method for achieving auto-adaptive clusteringin a sensor network, the method comprising: measuring a plurality ofparameters by a set of sensor nodes, wherein each of the sensor nodescomprising a plurality of sensors present at a plurality of locations inan area; sending a request, by a user interface, to the set of sensornodes to send the plurality of parameters measured at the set of sensornodes; sending the plurality of parameters to a sink node in response tothe request; performing a first level clustering, by a processor at thesink node, for grouping the set of sensor nodes into data level clustersbased on the measured plurality of parameters; performing a second levelclustering, by the processor at the sink node, for grouping the set ofsensor nodes in the data level clusters into location level clustersbased on the plurality of locations, wherein location of each of the setof sensor nodes is used as a criteria for sub-clustering each of thedata level clusters into the location level clusters, wherein the firstlevel and the second level clustering is based on an Adaptive DataCentric Clustering (ADCS) algorithm, wherein the ADCS algorithm isconfigured to use unsupervised learning algorithms for performing thefirst level clustering and the second level clustering, wherein theclustering is achieved by statistically measuring similarity levelsbetween measurements from the set of sensor nodes, wherein theclustering is performed on a predefined service level agreement (SLA)pertaining to precision of monitoring for the area covered by the set ofsensor nodes, and wherein each of the set of sensor nodes in each of thelocation level clusters include characteristics of measurements atsimilar levels and location close to other sensor nodes; designating, bythe processor at the sink node, a node in each of the data levelclusters and location level clusters as a cluster head to arrive at aclustering decision; and conveying, by the processor at the sink node,the clustering decision back to the set of sensor nodes to adaptivelyrearrange the set of sensor nodes in clusters.
 2. The method of claim 1,wherein the ADCS algorithm adjusts to nature of the cluster, size of thecluster and cluster member numbers.
 3. The method of claim 1, whereinthe ADCS algorithm is performed on the sink node.
 4. The method of claim1 further configured to allow the use of multiple algorithms to clusterin order to achieve improved performance.
 5. A system for achievingauto-adaptive clustering in a sensor network, the system comprises: aset of sensor nodes configured to measure a plurality of parameters,wherein each of the sensor nodes comprising a plurality of sensorspresent at a plurality of locations in an area; a user interface forgenerating a request by a user to send the measured plurality ofparameters; a sink node for receiving the plurality of parametersmeasured by the set of sensor nodes; a memory; and a processor incommunication with the memory, the processor further comprising: a firstlevel clustering module for performing a first level clustering forgrouping the set of sensor nodes into data level clusters based on themeasured plurality of parameters; a second level clustering module forperforming a second level clustering for grouping the set of sensornodes in the data level clusters into location level clusters based onthe plurality of locations, wherein location of each of the set ofsensor nodes is used as a criteria for sub-clustering each of the datalevel clusters into the location level clusters, wherein the first leveland the second level clustering is based on an Adaptive Data CentricClustering (ADCS) algorithm, wherein the ADCS algorithm is configured touse unsupervised learning algorithms for performing the first levelclustering and the second level clustering, wherein the clustering isachieved by statistically measuring similarity levels betweenmeasurements from the set of sensor nodes, wherein the clustering isperformed on a predefined service level agreement (SLA) pertaining toprecision of monitoring for the area covered by the set of sensor nodes,and wherein each of the set of sensor nodes in each of the locationlevel clusters include characteristics of measurements at similar levelsand location close to other sensor nodes; a designating module fordesignating a cluster head after the data and location level clusteringto arrive at a clustering decision; and a conveying module for conveyingthe clustering decision back to the set of sensor nodes to adaptivelyrearrange the set of sensor nodes in clusters.
 6. The system of claim 5,wherein each of the set of sensor nodes are in electrical communicationwith the plurality of sensors.
 7. The system of claim 5, wherein thesink node is configured to be used as a gateway to external environment.8. The system of claim 5, wherein the clustering can also happen at morethan two levels, wherein each level depends on the plurality ofparameters and the plurality of locations.
 9. One or more non-transitorymachine readable information storage mediums comprising one or moreinstructions which when executed by one or more hardware processorscause: measuring a plurality of parameters by a set of sensor nodes,wherein each of the sensor nodes comprising a plurality of sensorspresent at a plurality of locations in an area; sending a request, by auser interface, to the set of sensor nodes to send the plurality ofparameters measured at the set of sensor nodes; sending the plurality ofparameters to a sink node in response to the request; performing a firstlevel clustering, by a processor at the sink node, for grouping the setof sensor nodes into data level clusters based on the measured pluralityof parameters; performing a second level clustering, by the processor atthe sink node, for grouping the set of sensor nodes in the data levelclusters into location level clusters based on the plurality oflocations, wherein location of each of the set of sensor nodes is usedas a criteria for sub-clustering each of the data level clusters intothe location level clusters, wherein the first level and the secondlevel clustering is based on an Adaptive Data Centric Clustering (ADCS)algorithm, wherein the ADCS algorithm is configured to use unsupervisedlearning algorithms for performing the first level clustering and thesecond level clustering, wherein the clustering is achieved bystatistically measuring similarity levels between measurements from theset of sensor nodes, wherein the clustering is performed on a predefinedservice level agreement (SLA) pertaining to precision of monitoring forthe area covered by the set of sensor nodes, and wherein each of the setof sensor nodes in each of the location level clusters includecharacteristics of measurements at similar levels and location close toother sensor nodes; designating, by the processor at the sink node, anode in each of the data level clusters and location level clusters as acluster head to arrive at a clustering decision; and conveying, by theprocessor, the clustering decision back to the set of sensor nodes toadaptively rearrange the set of sensor nodes in clusters.
 10. The one ormore non-transitory machine readable information storage mediums ofclaim 9, wherein the ADCS algorithm adjusts to nature of the cluster,size of the cluster and cluster member numbers.
 11. The one or morenon-transitory machine readable information storage mediums of claim 9,wherein the ADCS algorithm is performed on the sink node.
 12. The one ormore non-transitory machine readable information storage mediums ofclaim 9, further configured to allow the use of multiple algorithms tocluster in order to achieve improved performance.